Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. In this work, we holistically treat the sparse nature of radar data by introducing an adaptive subsampling method together with a tailored network architecture that exploits the sparsity patterns to discover global and local dependencies in the radar signal. Our subsampling module selects a subset of pixels from range-doppler (RD) spectra that contribute most to the downstream perception tasks. To improve the feature extraction on sparse subsampled data, we propose a new way of applying graph neural networks on radar data and design a novel two-branch backbone to capture both global and local neighbor information. An attentive fusion module is applied to combine features from both branches. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation, meanwhile using sparse subsampled input data.

基于稀疏雷达数据的前瞻感知在自动驾驶中越来越受到关注，本论文通过引入自适应子采样方法和定制化的网络架构来综合处理稀疏雷达数据的特性，并利用稀疏模式发现雷达信号中的全局和局部相关性。实验证明，我们的SparseRadNet在目标检测方面超过了最先进的水平，并在自由空间分割方面几乎达到了最先进的准确性，同时使用了稀疏子采样输入数据。

SparseRadNet: 基于子采样雷达数据的稀疏感知神经网络